Instruction production

ABSTRACT

Systems, methods, and other embodiments associated with instruction production are described. In one example, a system can comprise a difference component that makes an identification of a difference between an actual action of a user and a standard action for the user. The system also can comprise an instruction component that produces an instruction to instruct the user to change from the action of the user to the standard action for the user, where production of the instruction is based, at least in part, on the difference. The system further can comprise a non-transitory computer-readable medium configured to retain the instruction. Additionally, the system can comprise an output component configured to cause disclosure of the instruction.

CROSS-REFERENCE

This application claims the benefit of and incorporates by referenceherein U.S. non-provisional patent application Ser. No. 13/769,385 whichwas filed on Feb. 17, 2013. This application is a continuation of, alsoclaims the benefit of, and incorporates by reference herein U.S.non-provisional patent application Ser. No. 15/201,555 which was filedon Jul. 4, 2016 and claims priority to U.S. non-provisional patentapplication Ser. No. 13/769,385.

BACKGROUND

A person can desire to gain a specific skill set and seek out a coachfor instruction of the specific skill. In a non-limiting example, theperson can desire to improve his or her golf swing. To improve his orher golf swing, the person (golfer) can contact a club professional at alocal golf club for lessons. The golfer can meet with the clubprofessional and the club professional can provide insight as to how thegolfer can improve his or her golf swing.

BRIEF DESCRIPTION OF THE FIGURES

The accompanying drawings, which are incorporated in and constitute apart of the detailed description, illustrate various example systems,methods, and other example embodiments of various innovative aspects.These drawings include:

FIG. 1 illustrates at least one embodiment of a system that includes adifference component, an instruction component, and an output component;

FIG. 2 illustrates at least one embodiment of a system that includes thedifference component, an analysis component, a selection component, theinstruction component, and the output component;

FIG. 3 illustrates at least one embodiment of a system that includes thedifference component, a search component, the instruction component, theoutput component, the analysis component, and the selection component;

FIG. 4 illustrates at least one embodiment of a system that includes thedifference component, an assessment component, an alteration component,the instruction component, and the output component;

FIG. 5 illustrates at least one embodiment of a system that includes thedifference component, an input component, the instruction component, andthe output component;

FIG. 6 illustrates at least one embodiment of a system that includes anobservation component, the difference component, the instructioncomponent, and the output component;

FIG. 7 illustrates at least one embodiment of a system that includes achoice component, the difference component, the instruction component,and the output component;

FIG. 8 illustrates at least one embodiment of a system that includes anevaluation component, the choice component, the difference component,the instruction component, and the output component;

FIG. 9 illustrates at least one embodiment of a system that includes thedifference component, the instruction component, the output component,and a surveillance component

FIG. 10 illustrates at least one embodiment of a system that includesthe difference component, the instruction component, the outputcomponent, the surveillance component, an investigation component, andan update component;

FIG. 11 illustrates at least one embodiment of a system that includes aprediction component, the difference component, the instructioncomponent, and the output component;

FIG. 12 illustrates at least one embodiment of a system that includes aprocessor and a non-transitory computer-readable medium;

FIG. 13 illustrates at least one embodiment of a first method;

FIG. 14 illustrates at least one embodiment of a second method;

FIG. 15 illustrates at least one embodiment of an example system thatcan function as part of a control system;

FIG. 16 illustrates at least one embodiment of a system that may be usedin practicing at least one aspect disclosed herein; and

FIG. 17 illustrates at least one embodiment of a system, upon which atleast one aspect disclosed herein can be practiced.

It will be appreciated that illustrated element boundaries (e.g., boxes,groups of boxes, or other shapes) in the figures represent one exampleof the boundaries. One of ordinary skill in the art will appreciate thatin some examples one element may be designed as multiple elements orthat multiple elements may be designed as one element. In some examples,an element shown as an internal component of another element may beimplemented as an external component and vice versa. Furthermore,elements may not be drawn to scale. These elements and other variationsare considered to be embraced by the general theme of the figures, andit is understood that the drawings are intended to convey the spirit ofcertain features related to this application, and are by no meansregarded as exhaustive or fully inclusive in their representations.Additionally, it is to be appreciated that the designation ‘FIG.’represents ‘Figure’. In one example, ‘FIG. 1’ and ‘FIG. 1’ are referringto the same drawing.

The terms ‘may’ and ‘can’ are used to indicate a permitted feature, oralternative embodiments, depending on the context of the description ofthe feature or embodiments. In one example, a sentence states ‘A can beAA’ or ‘A may be AA’. Thus, in the former case, in at least oneembodiment A is AA, and in another embodiment A is not AA. In the lattercase, A may be selected to be AA, or A may be selected not to be AA.However, this is an example of A, and A should not be construed as onlybeing AA. In either case, however, the alternative or permittedembodiments in the written description are not to be construed asinjecting ambiguity into the appended claims. Where claim ‘x’ recites Ais AA, for instance, then A is not to be construed as being other thanAA for purposes of claim x. This construction is so despite anypermitted or alternative features and embodiments described in thewritten description.

DETAILED DESCRIPTION

Described herein are example systems, methods, and other embodimentsassociated with instruction production. A golfer going to a clubprofessional can be time consuming, expensive, and have other negativeaspects. Therefore, it may be beneficial for the golfer to receiveinstruction from a system, such as an application on a mobile phone orother electronic device. The application can monitor how the golferswings his or her golf club and automatically compare the golfer's golfswing against a preferred golf swing, such as the swing of a leadingprofessional golfer. Based on a result of this comparison, theapplication can produce an instruction to the golfer.

In one example, the golfer can swing a golf driver (or utilize adifferent piece of sports equipment or other equipment in variousexamples and embodiments) and the application can monitor how the golferswings the club. The application can identify that the golfer'sbackswing of the club comes to an angle of x degrees with respect to apredetermined reference line (or determine rotation, distance, ratios,or other metrics in various examples and embodiments). The applicationcan include a video of an ideal golf swing of a professional golfer. Inthe professional golf swing, the professional golfer can have his or herbackswing come to an angle of x-y degrees. Therefore the application cangive an instruction to the golfer to change his or her backswing tobetter replicate the professional's swing. While aspects disclosedherein are described with golf-based examples or other specificexamples, it is to be appreciated by one of ordinary skill in the artthat these examples not intended to be limiting, and a physical motionwhere varying form can yield different results can be cognized under thedisclosures herein.

The following paragraphs include definitions of selected terms discussedat least in the detailed description. The definitions may includeexamples used to explain features of terms and are not intended to belimiting. In addition, where a singular term is disclosed, it is to beappreciated that plural terms are also covered by the definitions.Conversely, where a plural term is disclosed, it is to be appreciatedthat a singular term is also covered by the definition. In addition, aset can include one or more member(s).

References to “at least one embodiment”, “one embodiment”, “anembodiment”, “one example”, “an example”, and so on, indicate that theembodiment(s) or example(s) so described may include a particularfeature. The embodiment(s) or example(s) are shown to highlight onefeature and no inference should be drawn that every embodimentnecessarily includes that feature. Multiple usages of the phrase “in atleast one embodiment” and others do not necessarily refer to the sameembodiment; however this term may refer to the same embodiment. It is tobe appreciated that multiple examples and/or embodiments may be combinedtogether to form another embodiment. Where lists of samples orembodiments are provided, such lists are not intended to be exhaustivelistings, but rather provide one of ordinary skill in the art with aconceptual framework to understand various possibilities or classes tobe applied in the situation including options that may not be expresslylisted.

“Computer-readable medium”, as used herein, refers to a medium thatstores signals, instructions, and/or data. A computer may access acomputer-readable medium and read information stored on thecomputer-readable medium. In at least one embodiment, thecomputer-readable medium stores instruction and the computer can performthose instructions as a method. The computer-readable medium may takeforms, including, but not limited to, non-volatile media (e.g., opticaldisks, magnetic disks, and so on), and volatile media (e.g.,semiconductor memories, dynamic memory, and so on). Example forms of acomputer-readable medium may include, but are not limited to, a floppydisk, a flexible disk, a hard disk, a magnetic tape, other magneticmedium, an application specific integrated circuit (ASIC), aprogrammable logic device, a compact disk (CD), other optical medium, arandom access memory (RAM), a read only memory (ROM), a memory chip orcard, a memory stick, and other media from which a computer, a processoror other electronic device can read.

“Component” and the like as used herein, includes but is not limited tohardware, firmware, software stored or in execution on a machine, aroutine, a data structure, and/or at least one combination of these(e.g., hardware and software stored). Component, logic, module, andinterface may be used interchangeably. A component may be used toperform a function(s) or an action(s), and/or to cause a function oraction from another component, method, and/or system. A component mayinclude a software controlled microprocessor, a discrete logic (e.g.,ASIC), an analog circuit, a digital circuit, a programmed logic device,a memory device containing instructions, a process running on aprocessor, a processor, an object, an executable, a thread of execution,a program, a computer and so on. A component may include one or moregates, combinations of gates, or other circuit components. Wheremultiple components are described, it may be possible to incorporate themultiple components into one physical component. Similarly, where asingle component is described, it may be possible to distribute thatsingle component between multiple physical components. In at least oneembodiment, the multiple physical components are distributed among anetwork. By way of illustration, both/either a controller and/or anapplication running on a controller can be one or more components.

FIG. 1 illustrates at least one embodiment of a system 100 that includesa difference component 110, an instruction component 120, and an outputcomponent 130. The difference component 110 makes an identification of adifference between an actual action of a user 140 and a standard actionfor the user 150. The instruction component 120 produces an instruction160 to instruct the user to change from the actual action of the user140 to the standard action for the user 150, where production of theinstruction 160 is based, at least in part, on the difference. Theoutput component 130 causes disclosure of the instruction 160. In atleast one embodiment the difference component 110, the instructioncomponent 120, and the output component 130 are part of a mobile device.

Returning to the above example of the golf swing, the golfer (user) canprovide a test swing that is the actual action of the user 140. In atleast one embodiment, the test swing can be a series of swings, anaverage of swings, or a representative user swing modeled from a seriesof provided swings. The user can select a golf swing that the golferwould like to emulate, such as the golf swing of Tiger Woods (or anotherprofessional, or another form not associated with a professional), andthis golf swing of Tiger Woods becomes the standard action for the user150. The difference component 110 can compare the user's golf swing tothe golf swing of Tiger Woods. The instruction component 120 candetermine how the user should change his or her golf swing based on thecomparison. From this, the instruction component 120 can determine howthe user should change his or her golf swing to more emulate their swingto the swing of Tiger Woods. The instruction component 120 produces theinstruction 160 that instructs the golfer on how to change his or herswing and the output component 130 causes the instruction 160 to bedisclosed (e.g., the instruction 160 is displayed on a screen, theinstruction 160 is given audibly, et cetera). For example, the golfermay bend his or her knees less than Tiger Woods and therefore theinstruction 160 can be for the golfer to bend his or her knees more, tobend his or her knees z degrees, et cetera.

As has been suggested, instruction 160 can involve more than definingthe differences, but also describe aspects not necessarily directlyrelated to, in contact with, or the focus of a particular technique. Byway of example, a less experienced golfer may myopically view the endresult of a swing to be purely a product of arm motion. However, thestarting position, end position, and motion between both for head,shoulders, hips, legs and feet can be influential. In this regard, agolfer's movement can be carefully tracked and statistical analysisapplied to inputs and outputs at different stages of motion on differentportions of the body to determine the changes that can be implemented tomore accurately emulate a desired form.

In at least one embodiment, the actual action of the user 140 and thestandard action for the user 150 are for the same activity (e.g.,golfing, shooting a basketball, playing a musical instrument). In atleast one embodiment, the actual action of the user 140 is a physicalmovement of the user. In at least one embodiment, the standard actionfor the user 150 is a different activity for the user than for which theactual action of the user 140 is performed. For example, the user canpractice Taekwondo. Depending how the user executes specific moves ofthe Taekwondo, the system 100 can determine another martial art of theuser to practice. For example, two potential standard actions of theuser can be practice of Judo and practice of Brazilian Jiu-Jitsu (bothgrappling martial arts) while Taekwondo (a striking martial art) is theactual action of the user. The difference component 110 can compare theuser's Taekwondo against example moves of Judo and Brazilian Jiu-Jitsu.The instruction component 120 can determine which has a lesserdifference for the user—Judo or Brazilian Jiu-Jitsu.

A determination of lesser difference can be based on, for example, ascoring system (e.g., a scoring system used to determine the winner of acontest). The scoring system can capture numerical representations ofvarious types of motion and determine scores for particular motions.Motions recorded can be in two or more dimensions. In some embodiments,a plurality translational and rotational degrees of freedom, as well asthe particular accelerations and velocities associated with suchcomponents of motion, can be scored in isolation or together to generatescores associated with certain motions or techniques. Motions ortechniques found to have smaller differences between scores can bepreferred in the determination. In one example, if the user's Taekwondospecific moves would make the user more likely to learn Judo morequickly, then the instruction component 120 can create the instructionthat suggests that the user learn Judo and the output component 130 candisclose the instruction 160 accordingly.

In at least one embodiment, at least one sensor can be used forinformation and/scoring purposes. In one example, a sensor can be placedin a right and left boxing glove (e.g., wrist area of the glove so asnot to influence a punch) of two boxers that engage in an amateur orprofessional boxing match. The sensors can obtain and/or processinformation related to each boxer to determine punch strength, punchform, if a combination occurs, and other determinations. Thesedeterminations can be used to assign scores to the boxers.

In one example of the scoring system, punch form can be used. Forexample, the motion of a jab of a boxer with the sensor can bedetermined and compared against a form jab. Depending how close theboxer's jab is to the form jab can determine a number of points theboxer receives for the punch. The number of points can also beinfluenced by how the punch impacts the opposing fighter. For example, aform jab that misses an opponent can be given no score, a form jab thatmarginally impacts the opponent (e.g., impacts with low force, impactsat a less than desirable location, et cetera) can be given a relativelyhigh score, a non-form jab that marginally impacts the opponent can begiven a relatively low score, and a form jab that strongly impacts theopponent (e.g., impacts with a force above a threshold value, impactswithin a particular body or facial zone, et cetera) can be given aperfect score. The score given with punches can be combined withsubjective judging to produce a score, provided to judges for use indetermining round score, provided to broadcasters to give views moreinformation on how a fighter performs, et cetera.

In one example, the scoring system is used as part of a trainingsession. An amateur boxer can have a sparring session with anotheramateur boxer. The purpose of the sparring session can be to have theamateur boxer improve his or her form. In addition to form of the boxingpunches, the sensor can be used to score or provide other informationwith relation to foot placement, body movement, et cetera.

In at least one embodiment, if an acceptable standard action for theuser does not exist, then the instruction component 120 can create sucha standard action for the user. The instruction component 120 cancollect various information, such as information about the user,information about successful individuals performing the action, andother information and based on this information the instructioncomponent 120 can produce the standard action for the user 150. In oneexample, the user can desire to be a gymnast, but the user may not havethe body type of a prototypical gymnast (e.g., he or she may besignificantly taller than the prototypical gymnast). Due to thisdifference, a suitable and/or usable standard action for the user maynot exist. The instruction component 120 can use an existing standardaction for the user as a template and make modifications to the existingstandard action for the user to produce the standard action for the user150. With the created standard action for the user, the differencecomponent 110 can determine the difference between the created standardaction for the user and the actual action of the user 140, theinstruction component 120 can produce the instruction 160 based on thisdifference, and the output component 130 can cause the instruction 160to be disclosed.

In at least one embodiment, the technique of successful individualsperforming the action can be ignored if any exists. Instead, a standardaction can be determined through statistical analysis. A user canperform the action a plurality of times, and the result can be recorded,observed, and/or otherwise provided in a fashion similar to the providedor collected actual action. Various techniques of statistical analysisto be applied to determine correlation and/or causation of results basedon corresponding changes to the form of the action. Resultingstatistical models can be used to project improved form for the actionthat will give the user a more-desired result.

In at least one embodiment, the instruction 160 is, at least in part, aninstruction to use a particular piece of equipment. The system 100 canbe used in identifying an item the user should use. In one example, theuser can desire to purchase a new guitar. The user can play a guitaralready owned and this can be the actual action of the user. Variousfactors such as force used by the user to strike strings, quickness ofthe user to move his or her hand among different frets, and otherfactors of the actual action of the user 140 can be evaluated to find anaction (e.g., the standard action for the user 150) that produces apreferred (e.g., optimal) guitar sound. Based on a comparison, theinstruction component 120 can identify a guitar that would have improvedsound over the guitar already owned based on the user's guitar playingstyle. Therefore, the instruction can be what guitar to purchase, a listof preferable guitars, a ranked list of preferable guitars, et cetera.

As has been discussed, results can be provided for analysis. In theexample of a guitar, a microphone can be used to record audio data,which can be compared to ideal audio data or projected audio data basedon an audio input (e.g., music recorded in a mp3 file) or non-audioinput (e.g., sheet music or other detailed descriptions). In the exampleof a golf swing, a ball's coordinate location can be known, before,during, and after the swing to determine at least direction and distancebased on the form given. In at least one embodiment, the ball or othercomponents can include various positioning systems to expedite orincrease accuracy of such location-based analysis. In at least oneembodiment, image processing can be used in conjunction with still orvideo information relating to how and when a golf ball is struck and itstrajectory and changing velocity thereafter. Such examples focused inparticular skills are provided for illustrative purposes, and one ofordinary skill in the art will appreciate many other possibilitiesrelated to these and other techniques for determining and analyzingresults.

FIG. 2 illustrates at least one embodiment of a system 200 that includesthe difference component 110, an analysis component 210, a selectioncomponent 220, the instruction component 120, and the output component130. The analysis component 210 analyzes the difference and theselection component 220 selects the instruction 160 from among at leasta first instruction and a second instruction that are differentinstructions. The instruction 160 is produced in response to theselection of the instruction 160 and the selection is based, at least inpart, on the difference.

In one example, the user can have a running motion for long distanceruns that is the actual action of the user 140. The user can desire fora more efficient running motion that is the standard action for the user150. The first instruction can be to change upper body angle while thesecond instruction can be to change stride length. It may be easier forthe user to follow one instruction and therefore the first instructionor the second instruction can be selected and caused to be disclosed.For example, the first instruction can be caused to be disclosed. Theuser can change the actual action of the user 140 such that the upperbody angle is changed. Once this change is mastered, the secondinstruction can be selected and disclosed.

In one example, the user can be running in a marathon with a particularrunning motion that is the actual action of the user 140. As the userruns the marathon, the form of the particular running motion can breakdown causing the user to run slower. The system 200 can function toprovide an instruction to the improve form and the improved form is thestandard action for the user 150. Part of the form breakdown can be theuser's legs not being lifted as high due to lactic acid buildup. Thefirst instruction can be for the user to lift his or her legs higher.However, due to the lactic acid buildup, this may not be feasible forthe user. The system 200 can identify this infeasibility (e.g., throughbio-monitoring by way of sensors, by user response (e.g., the firstinstruction is given and the user rejects the first instruction), etcetera) and disregard the first instruction. The second instruction canbe for the user to change the motion with his or her arms. The system200 can identify that the second instruction is feasible and thus selectand cause disclosure of the second instruction.

In one example, the instruction 160 is one instruction or more than oneinstruction. A runner's form can be made up of many different elementssuch as stride length, back posture, arm movement, and other elements.The instruction 160 can be to change multiple elements of the user'sform and/or instruct the user to keep doing a certain aspect. Theinstruction 160 can be exclusively the first instruction or the secondinstruction as well as be both the first instruction and the secondinstruction. In at least one embodiment, the selection of theinstruction 160 by the selection component 220 is based, at least inpart, on a physical characteristic set of the user (e.g., user bodycharacteristics, real-time bio data of the user, injury information onthe user, et cetera).

FIG. 3 illustrates at least one embodiment of a system 300 that includesthe difference component 110, a search component 310, the instructioncomponent 120, the output component 130, the analysis component 210, andthe selection component 220. The search component 310 searches a sourceupon which to base the first instruction and the second instruction,where the first instruction is selected by the selection component 220and where the second instruction is not selected by the selectioncomponent 220. The search is based, at least in part, on the difference.

The search component 310 can find the instruction source. In oneexample, the user can provide the instruction source name and/orlocation and the search component 310 searches the information sourceconsistent with what the user provides. In one example, the searchcomponent 310 can proactively (e.g., automatically) determine theinstruction source to search.

While aspects disclosed herein relate to physical actions, it is to beappreciated by one of ordinary skill in the art that aspects can bepracticed actions that may not be considered physical actions. Feedbackcan be provided regarding a wide variety of behaviors or activities.

In one example, the user can request for an instruction on how to havemore meaningful online conversations. For example, the user can requestto have conversations with single females where the females give answersto questions that are longer in length. The analysis component 210 cananalyze online conversations of the user that serve as the actual actionof the user 140 while the standard action for the user 150 is asubjective standard by the user of more meaningful online conversations.Based on a result of the online conversation analysis, the searchcomponent 310 can search out for an information source that providesguidance on how to have better online conversations, samples ofconversations that have longer answers than that of the user, et cetera.The search component 310 can perform a search for the information sourcefor information upon which the base the first instruction and the secondinstruction. The search can be performed with the goal of findinginformation that can be used by the instruction component 120 to produce(e.g., generate, create, turnout, modify an existing instruction, etcetera) the instruction 160 to the user. The analysis component 210 cananalyze these instructions and the selection component 220 can selectthe first instruction.

In one example, the search component 310 can find and access aninstruction database that functions as the source. Returning to theonline conversation example, the instruction database can be an Internetwebsite with suggested questions to ask a female to facilitate continuedand engaging conversation. The search component 310 can find the firstinstruction and the second instruction from within the instructiondatabase. In making this find, the analysis component 210 can analyzethe difference and based on a result of the analysis the searchcomponent 310 can find the first instruction and the second instruction.The analysis component 210 can then analyze the first instruction andsecond instruction to determine if either the first instruction or thesecond instruction is a suitable instruction (e.g., done by way of ascoring system). If the first instruction or the second instruction isnot a suitable instruction, then the analysis component 210 can initiatethe search component 310 to find a third instruction. In at least oneembodiment, the third instruction can then be characterized as the firstinstruction with the original first instruction being disregarded. Thefirst instruction (formerly third instruction) can be analyzed,identified as suitable, and selected by the selection component 220. Theinstruction component 120 can designate the first instruction as theinstruction for use and this designation can function as production ofthe instruction 160. The output component 130 can cause the instruction160 to be disclosed (e.g., send a command for disclosure of theinstruction 160 to occur, disclose the instruction 160, et cetera).

In at least one embodiment, the instruction 160 can be developed, atleast in part, from a variety of sources. In at least one embodiment,models (e.g., mathematical models quantifying at least a portion ofactivity related to the instruction 160) can be developed in advance ofusing preexisting databases for employment. The instruction 160 can bedeveloped on-the-fly such as using data observed in a particular sessionor window of time (e.g., during use of the system 300). In at least oneembodiment, sources of data used in or for instruction 160 can havevarying levels. For example, a vendor's databases can have one level(e.g., “trusted source”) whereas community-developed databases can haveanother (e.g., “reviewed source”), and individual developed databasescan have another still (e.g., “unverified source”). Data from a trustedsource can be given more weight in developing the instruction 160 thandata from an unverified source (e.g., a conflict can be resolved infavor of the trusted source, data from the trusted source can be usedmore frequently than data from the untrusted source, data from thetrusted source can be scored higher than data from the unverifiedsource, et cetera).

FIG. 4 illustrates at least one embodiment of a system 400 that includesthe difference component 110, an assessment component 410, an alterationcomponent 420, the instruction component 120, and the output component130. The assessment component 410 determines if the difference isreversible, where the actual action of the user 140 is defined in termsof a deviation from the standard action for the user 150. The alterationcomponent 420 identifies an alteration for the standard action for theuser 150. The alteration component 420 functions after the determinationis that the difference is not reversible, where the alterationfacilitates an outcome that is similar to the outcome for the standardaction for the user 150 and where the instruction 160 is based, at leastin part, on the alteration.

In one example, the standard action for the user 150 can be a cookingrecipe, such as making a pasta sauce. The actual action of the user 140can be an action set taken by the user to follow the cooking recipe. Thecooking recipe can call for a teaspoon of pepper to be mixed into asauce base, but the user can add a tablespoon of pepper and mix in thetablespoon. The system 400 can identify this incorrect addition ofpepper and the assessment component 410 can determine that it would beimpractical to remove pepper from the recipe. As such, the alterationcomponent 420 can determine (e.g., by way of a scoring system, byanalysis of online reviews, et cetera) if the cooking recipe could bealtered to compensate for the extra pepper added and have a final pastasauce similar to the pasta sauce that is produced from following thestandard action for the user 150. In at least one embodiment, thecompensation is not an increase of quantities of other ingredients inproportion to the over-mixed ingredient. For example, the alterationcomponent 420 can determine that adding a certain quantity of a specificingredient can counteract the impact of the extra pepper in the pastasauce. Therefore, the instruction component 120 can produce theinstruction 160 such that the instruction 160 instructs that the certainquantity of the specific ingredient be added to the pasta sauce and theoutput component 130 can cause the instruction 160 to be disclosed. Inat least one embodiment, the cooking recipe cannot be altered tocompensate for the extra pepper and the output component 130 can cause anotice to be disclosed to the user of the situation. In at least oneembodiment, a user following a recipe can lack certain ingredients orsufficient quantities thereof (e.g., that are not in his or herkitchen), and the recipe can be altered to use alternatives orcompensate for a lack of a particular ingredient (e.g., that are in hisor her kitchen).

In at least one embodiment, when the difference is reversible theinstruction 160 is to undo the actual action of the user 140. Returningto the example of the cooking recipe, the cooking recipe can call for atablespoon of pepper, but the user puts in a teaspoon. The assessmentcomponent 410 can determine if this action is reversible (e.g.,impossible to be reversed, impractical to be reversed, reversal wouldnot have the desired outcome on end product, et cetera). For example,the assessment component 410 can determine that adding another teaspoonof pepper is reasonable and as such the difference (e.g., adding ateaspoon instead of a tablespoon) is reversible. In response, thealteration component 150 can notify the instruction component 120 thatthe instruction 160 should be generated detailing that two moreteaspoons of pepper should be added. This can occur without alterationto the standard action for the user 150. The instruction component 120can produce the instruction 160 and the output component 130 can causethe instruction 160 to be disclosed.

In one example, the cooking recipe can be for baking cookies. The firststep of the recipe can be to add a half stick of butter. However, theuser can add a stick of butter. The assessment component 410 candetermine that the user can remove half of the stick added and thereforethe difference is reversible. The alteration component 420 can identifythe alteration of the standard action for the user 150 as adding a stepof removing half the stick of butter. The instruction component 120 canproduce an instruction of removing half the stick of butter and theoutput component 130 can cause this instruction to be disclosed.

FIG. 5 illustrates at least one embodiment of a system 500 that includesthe difference component 110, an input component 510, the instructioncomponent 120, and the output component 130. The input component 510collects a goal input for the user. The instruction 160 facilitates thegoal input being met.

In one example, the user can have a goal of being able to slam dunk abasketball. The user can enter this goal into a graphical user interfacethat is part of the input component 510 as the goal input. Based on thisgoal input, the input component 510 can determine the standard actionfor the user 150. In one example, the difference component 110 canevaluate how the user performs a basketball layup, how close the usercomes to dunking the basketball, et cetera as the actual action of theuser 140. Based on a result of this evaluation, the input component 510can select the standard action for the user 150. In at least oneembodiment the user selects the standard action for the user 150 to meetthe goal input. In at least one embodiment, the input component 510evaluates the goal input and based on a result of this analysis theinput component 510 selects the standard action for the user 150. Inanother example, a pitcher can be pitching with a minor league baseballteam with the hope of reaching a Major League Baseball club withreaching the Major League Baseball club being the goal input. The inputcomponent 510 can evaluate various factors such as a parent club'scurrent roster, other prospects, and physical attributes of the user.The input component 510 can determine that the best way for the pitcherto reach the Major League Baseball club is to learn to throw a with asubmarine delivery since there is an absence of a submarine deliverypitcher on the parent club's roster as well as the rosters of otherclubs (e.g., for trade purposes). While this may not be the pitch thathas the highest ceiling and this may cause a higher likelihood ofinjury, adopting this type of delivery could be anticipated as the bestroute to the Majors. Based on this, the standard action for the user 150can be chosen as a textbook submarine delivery and/or the instruction160 can be consistent with the pitcher learning the submarine delivery.

Determinations can be made in at least this example using statisticalanalyses of leagues, teams, rosters, individuals, and thecharacteristics and/or performance thereof. Statistical information canbe sourced from publicly accessible news sources (e.g., sportswebsites), private sources (e.g., scouting or club databases), userinput, et cetera. In at least one embodiment, user input can includevideo provided or selected by the user that can be analyzed according topre-programmed packages or user-defined metrics. In the baseballexample, the minor league pitcher can import at least one video of oneor more major league pitchers from one or more angles and provide suchto at least a portion of software that analyzes the motion on at leastone video and determines statistical relationships and distinctions. Inat least one embodiment, a pitching plug-in can be used with thesoftware that is pre-programmed to the idiosyncrasies of baseballpitching. Such idiosyncrasies can be generalized or increasinglyspecific. An example of a generalized pitching plug-in is a plug-indesigned to identify trends in a group of successful pitchers (e.g.,pitchers that have spent several years on a Major League roster).Specifics can be used to filter or analyze pitchers sharing commoncharacteristics, such as dominant hand, height, weight, type of pitchand so forth. In at least one embodiment, a user can define baselinesfor statistical analysis. For example, the software can proactively(e.g., automatically) recognize different portions of Major Leaguepitchers in at least one video. In at least one embodiment, the user candefine at least one baseline (e.g., at least one point and/or arbitraryaxis from which various angles and/or distances can be measured, such asangle of different portions of the arm when the pitch is released, torsoor leg motion, pitch angle during flight, and so forth). Motion can beanalyzed over a single sample or a variety of samples to provide datafor statistical analysis that can then be applied to the motion of theuser.

In one example, the standard action for the user 150 is known and/or thedifference is known before the input component 510 collects the goalinput. For the example, how the user performs the basketball layup, howclose the user comes to dunking the basketball, et cetera can functionas the actual action of the user 140. In addition, the user can identifya video of Michael Jordan dunking a basketball that functions as thestandard action for the user 150. The difference component 150 cancompare the actual action of the user 140 against the standard actionfor the user 150 to produce the difference. The input component 510 canevaluate the difference, the actual action of the user 140, the standardaction for the user 150, other metadata (e.g., a goal input of a usersimilar in age, life state, school, et cetera), or a combinationthereof. Based on this evaluation, the input component can identify thegoal input, where identification of the goal input is a type ofcollection. In at least one embodiment, the input component 510 infersthe goal input through use of at least one artificial intelligencetechnique.

Long-term, step-wise plans can be developed using goal input and aplurality of actual actions of the user. In at least one embodiment, auser attempting to dunk a basketball can input or be observed performinga variety of movements to determine impediments to the performance of acomposite motion. For example, one or more basketball players capable ofdunking can be known to be capable at certain times to sprint adistance, high jump heights, and long jump distances. The user can beinformed of deficiencies with regard to relevant aspects of trainingrelated to dunking, and be provided instructions (e.g., at least aportion of a workout) designed to remedy the deficiencies. In anexample, the user can have an excellent long jump but a poor high jumpcompared to one or more basketball players capable of dunking, and thiscan be determined to be one factor influencing an inability to dunk froma particular position on the court. In addition to information aboutmodification to the dunking motion, instructions related to improvinghigh jump can be provided.

In an example, an entity other than the user enters the goal input. Inone example, a reality dancing competition can have celebritycontestants learning different dances, such as the waltz or the polka.Viewers of the competition can vote on which dances they would likedifferent celebrities to learn for the coming week. The input component510 can aggregate these votes and evaluate the aggregate result. Basedon the result of this evaluation, the input component 510 can select thegoal input (e.g., celebrity A performs the waltz while celebrity Bperforms the polka, celebrities A and B perform the polka, et cetera)and the system 500 can provide the instruction 160 on how to dance tothe respective celebrity, dance partner, show producer, et cetera

In one example, the system 500 is used as a tool to coach a footballteam. The actual action of the user 140 can include how the teamfunctions together (e.g., how the offense functions together) as well ashow individual members of the team function. The standard action for theuser 150 can be for the team to run a sweep off the right tackle. Theinput component 110 can evaluate metadata and determine that the goalinput is to run an outside play to the right. The instruction component120 can produce an instruction set of multiple instructions thatfunction as the instruction 160 for the coach on how to run the play(e.g., how a lineman should block, how fast the running back should run,et cetera).

FIG. 6 illustrates at least one embodiment of a system 600 that includesan observation component 610, the difference component 110, theinstruction component 120, and the output component 130. The observationcomponent 610 observes the actual action of the user 140, where theidentification of the difference performed by the difference component110 is performed after the actual action of the user 140 is observed bythe observation component 610. The observation component 610 can be partof, or leverage portions of (e.g., image capture hardware, processor,memory), the mobile device (e.g., along with the difference component110, the instruction component 120, and the output component 130). In atleast one embodiment, at least a portion of the observation component610 (or other components) can be available over a network such that themobile device transmits and receives information for action elsewhere.In at least one embodiment, all aspects can be local (e.g., embodied inor on, at the same physical location) to the mobile device. Hybridtechniques, such as solutions including some cloud-based resources andsome local resources can be applied without departing from the scopeherein.

In at least one embodiment, the observation component 610 can functionas a camera that views the user in action and based on this the actualaction of the user 140 is determined and thus observed. In one example,the user can perform a dance. The observation component 610 can viewthis dance and record a copy of the viewed dance (e.g., in anon-transitory computer-readable medium). Thus, the copy retains arecording of the actual action of the user 140.

In at least one embodiment, the observation component 610 can performprocessing with regard to the actual action of the user 140. In oneexample, the user can be performing an action among other users. In thisexample, the user can be an individual dance member of a chorus line.The observation component 610 can extract out actual actions of othermembers of the other members of the chorus line and identify the userand as such the actual action of the user 140. In at least oneembodiment, the observation component 610 can make a determination basedon the user's physical characteristics. In one example, how much theuser sweats, facial expressions of pain or exhaustion, and others can beobserved by the observation component 610 and this observation can beused by the instruction component 120 (e.g., if the user has a facialexpression consistent with pain, then the instruction component 120 candraw an inference that the instruction 160 should not push the userharder). Such aspects can be identified through statistical analyses ofsimilar aspects (e.g., facial expressions) that can be pre-programmed orprovided by users. In at least one embodiment, angles and movements tofacial features can reveal trends, and such trends can be associatedwith particular moods, feelings, or inferences. In one example, theobservation component 610 can make calculations on the user's physicalcharacteristics. The user's arms can be equal to z % of the user'sheight. Based on this arm to height ratio, the standard action for theuser 150 can be selected and/or the instruction component 120 canproduce the instruction 120.

In at least one embodiment, the observation component 610 functions as amotion sensor. In one example, the user can desire to improve his or hercycling motion when performing long distance cycling. The user canactivate (e.g., start observing motion) the application on his or hermobile device and/or attach the mobile device to their body, clothing,the bicycle, et cetera. In one example, the application can identifythat the user has begun cycling and self-activate. As the user rides,the observation component 610 can observe physical motion of the user,biometric data of the user, performance of the bicycle, et cetera. Theinstruction component 120, in producing the instruction 160, can takeinto account the difference as well as other information such as theperformance of the bicycle and/or the biometric data of the user.

In at least one embodiment, observation component 610 can be used withregard to an aspect of the standard action for the user 150. In oneexample, the user can have a particular golf swing that is the actualaction of the user 140 and the golf swing of Tiger Woods is the standardaction for the user 150. The mobile device with the motion sensor can beplaced in the pocket of the user. As the user swings, the motion sensor(that functions as the observation component 610) can monitor hipmovement of the user. Based on this hip movement, the instruction 160can be produced.

In at least one embodiment, the observation component 610 is coupled toa piece of equipment. For example, an apparatus that includes theobservation component 610 can be placed into part of a boxing glove(e.g., the wrist of the boxing glove). As the user punches with theglove, the movement of the hand, force of the hand, et cetera can bemeasured. The apparatus can also be used to understand placement of thehands when not punching, how much of a blow was absorbed by the gloves,movement of a boxer, et cetera In addition, the apparatus could be inthe right and left hand gloves of the boxer as well as in the gloves ofan opponent and/or sparring partner. These measurements can be used tounderstand the actual action of the user 140 that is used by thedifference component 110. The instruction component 120 can provide aninstruction 160 for use against a specific fighter (e.g., based onreadings from the apparatus in the gloves of the opponent and/orsparring partner). The apparatus can be used for other purposes, such asgathering punch data for display (e.g., by the output component 130),for use in scoring the boxing match (e.g., information given to judgesfor use in scoring, for use in an electronic scoring system, et cetera).

In at least one embodiment, a user can utilize techniques herein tomodify a technique for a particular opponent. In the above example, oneof the boxers can be highly successful and have excellent technique, butis expecting a match against a challenger whose technique counters thatof the boxer. The boxer can utilize systems and methods herein totemporarily or permanently modify an otherwise successful style toincrease the likelihood of success against the challenger. In oneexample, sensors can be placed in the right and left glove a fighter,the right and left shoe of the fighter, a mouthpiece of the fighter, anda cup hook and loop of the fighter. The fighter can spar with a partnerthat fights in a similar style to that of an upcoming opponent. Analysiscan be performed of how the fighter performs in the sparring session asboth a general style and for the style of the opponent. If the fighterperforms in a generally positive manner, but a poor manner in view ofthe opponent's style (e.g., the fighter normally does not move much andhas good hand defense, but this is a risky strategy due to the power ofthe opponent and the opponent's history of being able to get throughgood hand defense), then an instruction can be produced with the intentof changing style to better fit against the opponent. In at least oneembodiment, the instruction can be something that not only is easilylearned for the opponent, but can also be easily unlearned after thefight with the opponent so the fighter an return to the previouslysuccessful style.

In at least one embodiment, the user wears a suit with identificationpoints and the observation component 610 can observe the actual actionof the user through use of the suit with the identification points. Theidentification points can send signals for certain parts of the user'sbody, such as joints, end points (e.g., hands, feet, et cetera), etcetera The observation component 610 can collect these signals andidentify the actual action of the user 140 (e.g., the exact actualaction of the user or an approximation of the actual action of theuser). With the actual action of the user 140 identified, theinstruction component 120 can produce the instruction 160.

In at least one embodiment, a user does not wear a suit but appliesmarkers to their body. For example, different shapes can be drawn oraffixed on or to points of the body or equipment to facilitateidentification and/or analysis by observation component 610 or othercomponents. In at least one embodiment, various shapes, sizes, andcolors can be associated with different parts or aspects. In at leastone embodiment, tagging or marking can be performed electronicallywithout a physical suit or marker employed. For example, video can beimported and identified using various machine vision techniques ormanual identification of points of interest (e.g., body parts,equipment, orienting features within one or more frames, and others).Machine learning can be used to train at least a portion of the systemfor recognition. Automatic-sizing and/or scaling can occur based on thesize of known objects in a frame (e.g., known diameter of baseball). Inat least on embodiment, scaling can occur by other means such as userprovisioning or information related to the distance between a camera anda subject.

In at least one embodiment, a piece of equipment is adapted for use inthe system 600. For example, a golf club, baseball bat, helmet, ball,glove, shoe controller, et cetera, or portions thereof (e.g., head,grip, and so forth) can include one or more sensors, transmitters,receivers, or other components to facilitate information gathering andfeedback related to use of the piece of equipment. In at least oneembodiment, at least one intelligent element within or related to apiece of equipment can interact with at least one other component (e.g.,pedometer, GPS, gyroscope, accelerometer) in or related to otherequipment, or elsewhere associated with the user.

In at least one embodiment, various algorithms can be used to improvethe information collected by sensors. For example, a camera (orcomponents interacting there with) can include or employauto-stabilization and/or leveling techniques to ensure the quality ofimages collected. In one embodiment, an audio recording system (orcomponents interacting therewith) can include various filters orprocessing steps designed to reduce noise or increase the gain offrequencies at which the sound sought to be recorded exists.

FIG. 7 illustrates at least one embodiment of a system 700 that includesa choice component 710, the difference component 110, the instructioncomponent 120, and the output component 130. The choice component 710resolves (e.g., proactively resolves) the standard action for the user150 from a first standard action and a second standard action. The firststandard action and the second standard action can be different standardactions.

In one example, the actual action of the user 140 can be a baseballswing (e.g., swinging of a baseball bat) of the user and a goal of theuser can be to have an improved baseball swing. The user may not care inwhat manner their swing improves, just that their swing does improve.Improvements can be, for example, changes that are statistically likelyto raise or lower a metric in a desirable manner. In such an examplethat involves a baseball swing, similarities can be drawn to discoverthe technique and/or actions employed by batters who have such desirablemetrics (e.g., batting average, strikeouts, slugging percentage, homeruns, average speed of hit balls, swinging strikes per at-bat, andothers).

Different professional baseball players can have vastly differentbaseball swings. For example, a first baseball player can use arotational baseball swing while a second baseball player can use alinear baseball swing. Moving the swing of the user to a more productiverotational motion can meet the goal of an improved swing while movingthe swing of the user to a more productive linear motion can also meetthe goal. Therefore, the choice component 710 can decide if the standardaction for the user 150 should be a rotational swing or a linear swing.

In at least one embodiment, multiple standard actions can be identified.For example, a player can be taught to employ both a rotational andlinear swing depending on the situation. In another example, twodifferent linear swings can be identified depending on the pitcher'sdominant hand.

Different factors can be taken into account when the choice component710 chooses the standard action for the user 150. In returning to thebaseball example, if the actual action of the user 140 is already closerto the linear swing than the rotational swing, then the choice component710 can determine it would be most efficient if the linear swing was setfor the standard action of the user 150. Therefore, the linear swing canbe chosen by the choice component.

In one example, the user can desire for not just an improved swing, butan improved swing with specific attribute such as a swing with morepower. The rotational swing can be considered more powerful than thelinear swing, whereas the linear swing can be considered more of acontact swing. Based on this information, the choice component 710 canchoose the rotational swing since it is likely to provide the specificattribute desired.

In one example, various physical attributes of the user can be takeninto account by the choice component 710 in choosing the standard actionfor the user 150. The user can have longer arms than an average baseballplayer and these longer arms can influence a likelihood of success for aparticular swing. In baseball, a limited amount of time is available forreaction to a pitch on if a hitter will take a swing at the pitch. Thelonger time a swing takes, the less reaction time is available. If therotational swing is considered a longer swing than the linear swing andif the user has longer than average arms, then use of the rotationalswing by the user may leave too little time for reaction. Therefore, thechoice component 710 can choose the linear swing.

In one example, the choice component 710 can take into account physicalhealth of the user. Different nuances of a baseball swing can causedifferent results on the movement of the user's body and as such putdifferent stresses and strains on muscles, bones, joints, et cetera. Inthis example, the user can be an accomplished, yet aging professionalbaseball player. The actual action of the user 140 can be a linear swingwith specific characteristics (e.g., location of the right elbow inrelation to the torso during the swing, starting point of the swing fromthe user's batting stance, et cetera). Due to the player aging, physicalreaction times may be slowed and/or the player may be more prone toinjury. Therefore, the choice component 710 can choose the standardaction for the user 150 to be the linear swing, but with characteristicchanges to give more time for reaction time, lessen the likelihood ofinjury, avoid aggravation of existing or previous injuries, et cetera.Based on the selection of the standard action for the user 150 chosen bythe choice component 710, the difference component 110 can havesomething to compare the actual action of the user 140 against and thusidentifies the difference used by the instruction component 120 toproduce the instruction 160.

FIG. 8 illustrates at least one embodiment of a system 800 that includesan evaluation component 810, the choice component 710, the differencecomponent 110, the instruction component 120, and the output component130. The evaluation component 810 evaluates the actual action of theuser 140 to produce an evaluation result. The choice component 710proactively (e.g., automatically, in response to a request for theinstruction 160, et cetera) makes the choice of the standard action forthe user 150 based, at least in part, on the evaluation result.

The evaluation component 810 can evaluate how the user performs aspecific task that functions as the actual action of the user 140 andthis evaluation can produce the evaluation result. The observationcomponent 610 of FIG. 6 can observe how the user swings the club andmake a record that is retained in the non-transitory computer-readablemedium. The evaluation component 810 can evaluate the record todetermine characteristics of the record to produce the evaluationresult. The choice component 710 can access the evaluation result andbased on this evaluation result the choice component 710 can perform asearch for at least one standard action for the user 150 that can bechosen. If the choice component 710 finds the first standard action forthe user and the second standard action for the user, then theevaluation component 810 can evaluate the first standard action for theuser and the second standard action for the user and based on thisevaluation, along with the evaluation of the actual action of the user,the choice component 710 can choose the standard action for the user150.

In one example, the user can desire to swing a golf club such that agolf ball travels a certain distance and the user can request that he orshe swing a golf club more like Tiger Woods. The evaluation component810 can evaluate how the user swings the club and based on how the userswings the club the choice component 710 can select a golf swing uponwhich the user should model his or her golf swing. The choice component710 can evaluate swings of Tiger Woods at different points of his careerand choose a golf swing for the difference component 110 to use as thestandard action for the user 150.

As implied above, a plurality of goals can be identified. A user canseek to modify a technique according to two or more factors, such asreflecting the technique of a particular player while maximizing aparticular quality separate from the particular player's technique. Inat least one embodiment, a user can accept a trade-off in one metric toimprove another. In an example, the user can indicate they are willingto accept a possible reduction in the distance of their drives toimprove control over direction and/or employ a different club.

The system 800 can function to go against the wishes of the user.Returning to the example in the previous paragraph, the user can requestthat he or she swing the golf club more like Tiger Woods. The evaluationcomponent 810 can evaluate the actual action of the user 140 anddetermine that the user's swing is far from that of Tiger Woods, but isrelatively close to the swing of Bubba Watson. This can be done by theevaluation component 810 comparing the action of the user 140 againstthe swings of Tiger Woods, Bubba Watson, as well as other professionaland/or notable (e.g., Bobby Jones) golfers. Statistical analysis basedon distances, angles, performance metrics, et cetera, can be used todetermine a difference measure (or plurality of difference measures in adifference index). A technique with a minimum difference can bepreferred, and techniques with larger differences can be rejected orassociated with a warning. In at least one embodiment, statisticaltechniques (e.g., correlation, dependence, et cetera) can be used todetermine similar or different techniques.

The choice component 710 can determine that the user may have moresuccess emulating the swing of Bubba Watson than Tiger Woods. In atleast one embodiment, the choice component 710 can suggest to the user(e.g., through a user interface) that the user use the swing of BubbaWatson as the standard action for the user 150. If the user rejects thesuggestion, then the swing of Tiger Woods can be chosen as the standardaction for the user 150. If the user accepts the suggestion, then theswing of Bubba Watson can be chosen as the standard action for the user150. The choice component 710 can also forgo asking the user and choosethe swing of Bubba Watson for the standard action for the user 150despite the request of the user to have a swing similar to that of TigerWoods. The instruction component 120 can therefore produce theinstruction 160 such that it is consistent with facilitating the user'sgolf swing to be more like that of Bubba Watson.

FIG. 9 illustrates at least one embodiment of a system 900 that includesthe difference component 110, the instruction component 120, the outputcomponent 130, and a surveillance component 910. The surveillancecomponent 910 makes a surveillance related to how the user follows theinstruction 160. The instruction component 120 is configured to producea subsequent instruction, where the subsequent instruction instructs theuser to change to the standard action for the user 150.

In some instances, a user switching from the actual action of the user140 to the standard action for the user 150 can be a relatively simpletransition for the user. However, the transition can also be quitecomplex and difficult for the user. Therefore, the instruction 160 canbe a first instruction and after the user modifies the actual action ofthe user 140 to a certain extent the instruction component 120 canproduce a subsequent instruction. In this, the difference component 110can compare an updated version of the actual user action 140 and compareit against the standard action for the user 150. This difference can beused by the instruction component 120 to produce the subsequentinstruction and the output component 130 can cause disclosure of thesubsequent instruction.

In at least one embodiment, the system 900 can produce the instruction160 that is intended to be the first instruction in an instruction set.In one example, a baseball pitcher changing his or her throwing motioncan be a complex and intricate series of measureable changes. Theinstruction 160 can be for the pitcher to make a change to a certainpart of his or her throwing motion. One or more next instructionsprovided can be based on how, quantitatively or qualitatively, thechange impacts other aspects of the pitcher's throwing motion.Therefore, the difference component 110 can determine a differencebetween the changed actual action of the user and based on that theinstruction component 120 can produce the subsequent instruction.

In at least one embodiment, the difference component 110 can determinethat after following the instruction 160 the actual action of the user140 and the standard action for the user 150 are identical and/or thedifference is inconsequential. As such, the difference can be considerednone or virtually none and based on this the instruction component 120can instruct the output component 130 to send a complete message and/orthe instruction component 120 does not produce the subsequentinstruction. In an example, a player can have a difference in thetechnique of delivering a motion, but yield the same speed, accuracy,precision, et cetera. In such examples, while differences exist, the endresult is the same, and at least one difference among a plurality ofdifferences can be null. In this regard, differences among a pluralityof differences can be weighted or prioritized in order to avoid wastedeffort on changes that harm a desired outcome or do not improve at leastone parameter. In one example with a baseball swing, changing a firstdifference can substantially increase power while slightly loweringcontact with the ball while changing a second difference can causesubstantially higher contact with the ball, but slightly decrease power.A youth baseball player can attempt to change his swing because theplayer is not making very much contact with the ball and oftentimes isstriking out in games. While the first difference can benefit theplayer, the lower contact impact of the change can cause the player tomake even less contact can cause the player to become more frustrated,become subject of ridicule of teammates, et cetera and this may causethe player to quit the game. Therefore, the second difference can beprioritized since that will increase contact and cause the player tolikely enjoy the game more. Further, due to the already low contact withthe ball, the player may be unlikely to notice the decrease in power.Therefore, a first instruction can promote change according to thesecond difference. Once the player follows the first instruction andimproves his swing, a second instruction can be used to promote changeaccording to the first difference. Thus, the outcome can have the playerimprove his swing with as positive of a process as possible.

In at least one embodiment, the instruction 160 can be an instructionset that includes a first instruction and a second instruction. The usercan attempt to follow the first instruction, but the user following thefirst instruction may not be as expected as determined by the differencecomponent 110. In view of this, the instruction component 120 can alterthe second instruction (e.g., before or after being caused to bedisclosed by the output component 130) and the altered secondinstruction can be caused to be disclosed by the output component 130.

In at least one embodiment, the subsequent instruction can be areplacement for the instruction 160. In one example, the user canattempt to follow the instruction, but fail in execution of theinstruction. In this example, the instruction 160 can be to have theuser bend his or her back forward d degrees, but the user is limited inbending his or her back forward d-5 degrees. The surveillance component910 can identify the user's difficulty in following the instruction andsend a notice to the difference component 110 and/or the instructioncomponent 120. The difference component 110 can determine the differencein view of the user's limitation and/or the instruction component 120can produce the subsequent instruction in view of the user's limitation.The output component 130 can cause the subsequent instruction to bedisclosed.

FIG. 10 illustrates at least one embodiment of a system 1000 thatincludes the difference component 110, the instruction component 120,the output component 130, the surveillance component 910, aninvestigation component 1010, and an update component 1020. Theinvestigation component 1010 makes an investigation related to how theuser follows the instruction. The update component 1020 updates a logic(e.g., artificial intelligence logic) used by the instruction component120 for use in production of a subsequent instruction, where the updateis based, at least in part, on the surveillance.

In at least one embodiment, the instruction 160 and the subsequentinstruction are not for the same actual action of the user 140 and/ornot for the same standard action for the user 150. The investigationcomponent 1010 can determine that the user responds well to audio-videoinstruction and responded poorly to video instruction without audio. Theinvestigation component 1010 can use, for example, statistical analysistechniques over databases, spreadsheets, or other quantified records ofmovement allowing comparison of instruction 160 and the actual action ofthe user 140. In at least one embodiment, computer vision, machinelearning, and/or artificial intelligence can employ an image-onlytechnique that compares and/or overlays two or more images to compare,for example, instruction 160 and actual action 140. The update component1020 can update the instruction component 120 such that the instructioncomponent 120 produces subsequent audio-video instructions for the user.Therefore, the update component 1020 can update the logic of theinstruction component 120 such that the instruction component 120 isbetter tailored to a specific user.

Tailoring can include, for example, configuring the instructioncomponent 120 to provide information in a manner for which statisticalsupport exists to indicate the user will closer reflect the desiredoutcome faster or more accurately. In at least one embodiment, a usercan learn at different rates depending on medium or technique ofinstruction, and the user can select instruction to reflect a particulardesire as to learning rate. For example, the user may learn fastest bytechnique A, reflecting a first accuracy and first precision range ofmovements with 2 hours of instruction. Continuing in the same example,the user may learn more accurately by technique B, a second, higheraccuracy and a second, higher precision in 8 hours. Depending on theuser's wishes with regard to speed of training (e.g., bowling tournamenttomorrow versus in one month), a particular technique could be manuallyor automatically selected. For example, if technique B has a greateraccuracy outcome than technique A and the user's schedule indicates thatthere is time to learn technique B, then a component can proactivelyselect technique B for the user.

In at least one embodiment, tailoring can occur during a set-up phase orthroughout use of one or more systems and methods herein. For example, auser can indicate time frames, events, and so forth to allow a system toprefer one type of instruction to another. For example, a new flyfisherman may not know what is required to cast or how long it will taketo learn, but is aware he will be fly fishing with his boss in twomonths. Systems and methods herein can infer (e.g., using inferentialstatistics that are predetermined or developed through previousobservation and/or analysis of the user) various instructions andmilestones to best prepare the user for the trip in two months. Thisinferred training plan can thereafter be adjusted during the two monthsto accommodate the user's unique learning curve, adherence to a scheduleof instruction and/or any given instruction, changes to the timeline(e.g., changed timing of trip), et cetera.

The update component 1020 can also update the logic of the instructioncomponent 120 such that the instruction component 120 is more tailoredto a small group of users (e.g., golfers of the same gender, birth year,golf handicap, swing nuances, et cetera).

In one example, with the instruction 160 the actual action of the user140 can be for a golf swing and the standard action for the user 150 canthe golf swing of Tiger Woods. The instruction can be for the user toincrease the angle of their backswing, but the user can have difficultywith this due to lack of back flexibility. The investigation component1010 can identify this difficulty and the update component 1020 can, inresponse to the identification of this difficulty, cause the logic ofthe instruction component 120 to consider limited back flexibility insubsequent instructions. At another time, the actual action of the user140 can be a tennis backhand and the standard action for the user can bea generic backswing with a result of more accuracy. When the instructioncomponent 120 produces the instruction 160 with regard to the tennisbackhand, the updated logic with cause the instruction to put lessemphasis on back flexibility than would have been without the update.

In at least one embodiment, at least one component disclosed herein(e.g., the instruction component 120, the choice component 710 of FIG.7, et cetera) is located at a central server that communicates withdifferent client devices (e.g., mobile devices). The different clientdevices can access the instruction component 120 and request theinstruction 160. As feedback is gained from users at these differentclient devices following various instructions, the update component 1020can update the logic of the instruction component 120 such that theinstruction component produces improved instructions. In at least oneembodiment, the update component 1020 is located at the central serverwhile different client devices have individual instruction components120. In one example, the update component 1020 can determine a globalupdate and push the update to the instruction components 120 at thedifferent client devices. In one example, the update component 1020 candetermine an update for an individual instruction component 120 or asubset of instruction components 120 (e.g., less than a full set ofinstruction components serviced by the update component 1020) and thisupdate can be pushed to the appropriate instruction component 120 orsubset of instruction component 120.

FIG. 11 illustrates at least one embodiment of a system 1100 thatincludes a prediction component 1110, the difference component 110, theinstruction component 120, and the output component 130. The predictioncomponent 1110 predicts a future actual action of the user. Theinstruction component 120 takes the future actual action of user intoaccount in the production of the instruction 160.

In at least one embodiment, an anticipation of deterioration of theuser's body with age can be taken into account. The actual action of theuser 140 can be long-distance running, such as running in a 10 kilometerrace (a 10 k). In one example, the user can be near retirement age. Theprediction component 1110 can predict that in the future the user willhave less flexibility when they run as they age. The instructioncomponent 120 can produce the instruction 160 such that a stride learnedcurrently will also work in the future as the user has less flexibility.In one example, the user can be a teenager. A medical professional maybelieve it is detrimental for a teenager, specifically a youngerteenager, to run a race as long as a 10 k. The instruction 160 can befor the teenager not to run the race (e.g., the instruction component120 evaluates the standard action for the user 150 of a world championrunner and determines that the impact on the body of the teenager wouldbe too great). The instruction 160 can be for the teenager to run withminimal or lowered impact on long term health. Thus, the standard actionfor the user 150 can be a healthy or relatively healthy running stylefor the teenager.

In at least one embodiment, an anticipation of deterioration of theuser's body over an activity is taken into account. In one example, theactual action of the user 140 is the user's stride while running thefirst miles in a marathon. As the user runs, various instructions can beprovided to the user (e.g., the instruction 160). The initialinstructions can take into account that the user has many miles ahead.Therefore, the instructions can be for movements that conserve energy ofthe user since the future actual action of the user will be continuedrunning.

In at least one embodiment, the prediction component 1110 can anticipatethat as a runner runs in a marathon, lactic acid will build in his orher legs and as such it will be more difficult for the user to movetheir legs. Therefore, the instruction component 1110 can produce theinstruction 160 such that lower lactic acid levels are achieved as theuser runs. In at least one embodiment, the prediction component 1110 canview a user medical history and determine that later in races the userhas knee pain from continued impact on pavement. The instructioncomponent 160 can produce the instruction 160 such that a movement isselected to cause knee pain to be lowered. In at least one embodiment,the prediction component 1110 can anticipate that the user will losecertain form elements in their running stride as the user runs moremiles of the marathon. Therefore, the instruction 160 can be produced inanticipation of this loss of form and/or to have the loss of form occurat a latest time possible. In at least one embodiment, the system 110can be incorporated in a device that includes a map application (e.g.,map website, map database, et cetera) and a global positioning system.The instruction 160 can be different if it is anticipated that therunning will run mostly uphill as opposed to mostly downhill (which canbe determined by way of the map application and the global positioningsystem).

In at least one embodiment, an anticipation of how the user will improveis taken into account. The actual action of the user 140 can be a golfswing and the standard action for the user can be the golf swing ofTiger Woods. While the overall goal can be for the user to emulate theswing of Tiger Woods, the growing process to reach that goal can bedifficult and frustrating. The prediction component 1110 can predictthat the user will not have success for the first q number of monthsafter following a first instruction when swing transition includesmultiple instructions. The prediction component 1110 can also predictthat the user has a certain likelihood of becoming tired of not beingsuccessful. Based on this information, the instruction component 120 canproduce the instruction 160 (e.g., select a particular instruction overat least one other possible instruction) to minimize initial change forthe actual action of the user 140 (e.g., even if this adds to an overallnumber of instructions of lengthens time for the user to reach thestandard action for the user 150), attempt to have the actual user suchthat the user's golf score stays as low as possible, et cetera Theinstruction component 120 can retain the instruction in a non-transitorycomputer-readable medium and the output component 130 can cause theinstruction to be disclosed (e.g., texted to a mobile device associatedwith the user).

In at least one embodiment, medical databases or purpose-built databasescan be leveraged to determine ages or conditions at/under which injury,deterioration, rate of healing, and other physical risks manifest orsubside. Based on user information (e.g., age, height, weight, previousinjuries or conditions, and others), training plans can be customized tominimize risks or facilitate recovery based on a user's place in a riskdatabase generated from at least the medical or purpose-built databases.The user's seeding in the risk database can change based on observedperformance and over time (e.g., as a user ages, their placement in thedatabase can change) or events (e.g., if the user suffers an injury,then their placement in the database can change). In at least oneembodiment, the user can report a condition or injury, and a trainingplan can be adjusted in turn. In at least one embodiment, at least oneof a user's body composition or an estimated body composition can beused to estimate forces and stresses applied to particular portions of abody (e.g., knee joints). Training can be customized to select motionsor activities that minimize use or wear to particular portions of thebody based on injury or estimated risk. In addition, user injury riskcan be balanced against other goals. For example, a professional athletemay desire to shorten a recovery schedule even if that means a risk ofre-injury is greater due to a limited window in which the professionalathlete can make money. In addition, observed following of training andother learned information can populate the database, change how training(e.g., training instructions) is produced, et cetera.

FIG. 12 illustrates at least one embodiment of a system 1200 thatincludes a processor 1210 and a non-transitory computer-readable medium1220. In at least one embodiment, the processor 1210 and/or thenon-transitory computer-readable medium 1220 can individually be part ofvarious systems disclosed herein. For example, the non-transitorycomputer-readable medium 1220 can be part of the system 100 of FIG. 1(e.g., with or without the processor 1210) and can retain theinstruction 160 of FIG. 1. In at least one embodiment, the processor1210, the non-transitory computer-readable medium 1220, and/or anothercomponent disclosed herein can be part of the mobile device discussedwith regard to FIG. 1. In at least one embodiment, a component disclosedherein can include the processor 1210 and/or the processor 1210 canfunction as a component disclosed herein (e.g., preform processing ofthe observation component 610 of FIG. 6).

In at least one embodiment, the non-transitory computer-readable storagemedium 1220 is communicatively coupled to the processor 1210 and storescomputer executable components to facilitate operation of the componentscomprising a variance component, a recommendation component, and acausation component. The variance component is configured to identify avariance between an item and a desired outcome for the item. Therecommendation component is configured to make a recommendation on howto change the item to be more in line with the desired outcome for theitem, where the recommendation is based, at least in part, on thevariance. The causation component is configured to cause revelation ofthe recommendation (e.g., display on a monitor, cause audio presentment,et cetera).

In one example, a writer can write several chapters of a book and bestruck with writer's block on how to continue with the book and thushave a partially completed book. The variance component can analyze thepartially completed book to determine a book style, book genre, andother information. Based on this information, the variance component cansearch out completed books (or a single completed book) similar to thechapters, where the sought out books are commercially successful,critically acclaimed, et cetera. Such aspects can be determined usingvarious statistical analyses or searching techniques. In at least oneembodiment, books (or other media) can be analyzed and assigned a“fingerprint” based on an analysis algorithm, from which similarities ordistinctions can be gleaned. In at least one embodiment, media can bemanually rated or associated with particular qualities. Hybridtechniques utilizing machine learning or other techniques can beemployed to develop stored information against which to analyze thewriter's style or develop suggestions. The variance component cancompare the found book or books with the partially completed book andbased on this comparison the recommendation component can recommend howthe writer should move forward with the partially completed book. Forexample, the recommendation can be to have the love interests marry ifthe writer's block is at the point on if they should move forwardtogether or drift apart.

In one example, an advertisement can be evaluated to determine asimilarity of the advertisement to other advertisements, where thedesired outcome is that the advertisement has a positive impact onpotential consumers. If the advertisement is too similar toadvertisements of competitors, then the advertisement may not have thepositive impact desired. The recommendation component can proactivelydetermine how the advertisement should be changed to make theadvertisement more distinct while having the positive impact. Thecausation component can proactively cause the recommendation to berevealed (e.g., generate a report that includes the recommendation). Inat least one embodiment, the causation component can proactively causethe recommendation to be implemented upon the advertisement.

In one example, an artist can produce a painting that he or she believesis complete. The artist can submit a sample painting that the artistwould like his or her painting to be similar to as the desired outcome.The variance component can compare the painting with the sample paintingto determine differences (or single difference) between the painting andthe sample painting. The recommendation component can recommend how thepainting could be changed to be more similar to the sample painting orrecommend that the painting is similar enough to the sample painting andas such the recommendation is that no change should be made. Thecausation component can disclose this recommendation.

In one example, a musical artist can write a song and have specificmetrics for the song such as length, vocal range for the singer, etcetera. The variance component can compare the song (e.g., a performanceof the song, sheet music of the song) against the specific metrics thatfunction as the desired outcome. The recommendation component canidentify how the change the song and recommend the identified changes.The causation component can disclose the identified changes by modifyingthe song and playing the modified song for the musical artist. Themusical artist can use an interface to accept the changes, accepts partof the changes, make further changes, reject the changes, et cetera.

In one example, a student can try to solve a mathematical problem on achalkboard that is the item. The observation component 610 of FIG. 6 canidentify that that student is performing a step wrong that is the actualaction of the user 140 as compared to a correct solution that is thedesired outcome for the item. The variance component can compare thestep against the correct solution and the recommendation component canmake a recommendation to the student on how the correct the wrong step.This recommendation can include telling the student the correct step,informing the student that the step is wrong, playing a lesson for thestudent, et cetera. The causation component can reveal therecommendation.

While particular examples have been provided, it is to be appreciatedthat instances directed to sports, art, or other tasks are provided tosuggest the spirit of aspects herein rather than provide a literal orexhaustive listing. One of ordinary skill in the art will appreciate howexamples directed to a writer can be modified to apply to an athlete,how examples directed to an athlete can be modified to apply to amusician, and so forth.

The following methodologies are described with reference to figuresdepicting the methodologies as a series of blocks. These methodologiesmay be referred to as methods, processes, and others. While shown as aseries of blocks, it is to be appreciated that the blocks can occur indifferent orders and/or concurrently with other blocks. Additionally,blocks may not be required to perform a methodology. For example, if anexample methodology shows blocks 1, 2, 3, and 4, it may be possible forthe methodology to function with blocks 1-2-4, 1-2, 3-1-4, 2, 1-2-3-4,and others. Blocks may be wholly omitted, re-ordered, repeated or appearin combinations not depicted. Individual blocks or groups of blocks mayadditionally be combined or separated into multiple components.Furthermore, additional and/or alternative methodologies can employadditional, not illustrated blocks, or supplemental blocks not picturedcan be employed in some models or diagrams without deviating from thespirit of the features. In addition, at least a portion of themethodologies described herein may be practiced on a computer-readablemedium storing computer-executable instructions that when executed by aprocessor cause the processor to perform a methodology (e.g., method).

FIG. 13 illustrates at least one embodiment of a first method 1300. At1310, observing an actual action of a user occurs and at 1320,collecting a standard action for the user occurs. At 1330, making anidentification of a difference between the actual action of the user andthe standard action for the user occurs. Producing an instruction forthe user to instruct the user to change from the action of the user tothe standard action for the user occurs at 1340, where the production ofthe instruction is based, at least in part, on the difference. At 1350,causing disclosure of the instruction occurs. Actions are notnecessarily required to be performed in the order listed. For example,the standard action can be collected before the actual action isobserved.

FIG. 14 illustrates at least one embodiment of a second method 1400. At1405, a request is received (e.g., from the user, from a coach orinstructor, from an automated system, et cetera) for an instruction andthe action to receive the instruction is identified at 1410. The actionis analyzed at 1415 and a standard for the action is identified at 1420.The standard can be expressly given, found, found and then verified(e.g., by the user as acceptable), et cetera. The action and standardcan be compared to one another at 1425 and a check can occur if adifference exists at 1430. If the difference does not exist or is notconsidered substantial (e.g., objective metric and/or subjectivemetric), then a notice can be given that no change in action shouldoccur.

If the difference does exist or is substantial, then the difference canbe analyzed at 1435 and an instruction can be produced at 1440 based, atleast in part, on analysis of the difference. The instruction can alsobe produced (e.g., generated, found, selected, et cetera) based on theaction, the standard, a user request, user biometric data, or other datathat may or may not include the difference. The instruction can bedisclosed at 1445 and how the user follows the instruction can bemonitored at 1450. An inference can be drawn on how the user follows theinstruction (e.g., the user ignores the instruction, the user has greatsuccess in following the instruction, et cetera). Based on thisinference, a determination can be made on if logic should be changed at1455 used to produce the instruction. If the determination is that thelogic should not be changed, a result of the inference and/or a monitorresult can be recorded and used at a later time if further evidencearises. If change is appropriate, the change can be determined at 1460and enacted at 1465, the logic with the change can be tested at 1470, asubsequent instruction can be produced at 1475, and the subsequentinstruction can be disclosed at 1480.

FIG. 15 illustrates at least one embodiment of an example system 1500that can function as part of a control system 1510. The system 1500 caninclude at least one component disclosed herein and the control system1510 can be a mechanical control system, electrical control system,analog control system, digital control system, software control system,et cetera. The input can be commands (e.g., computer source code,computer executable code, et cetera) used by the instruction component120 of FIG. 1 to produce the instruction of FIG. 1 while the instruction160 of FIG. 1 and how the user follows this instruction can be theoutput. Based, at least in part, on how the user follows the instruction160 of FIG. 1, feedback can be obtained that can be used to change thecommands.

FIG. 16 illustrates at least one embodiment of a system 1600 that may beused in practicing at least one aspect disclosed herein. The system 1600includes a transmitter 1605 and a receiver 1610. In one or moreembodiments, the transmitter 1605 can include reception capabilitiesand/or the receiver 1610 can include transmission capabilities. In atleast one embodiment, the system 100 of FIG. 1 includes the transmitter1605 and/or the receiver 1610. In at least one embodiment, thetransmitter 1605 functions as at least part of the output component 130of FIG. 1. In at least one embodiment, the receiver functions as atleast part of the input component 510 of FIG. 5 to receive the goalinput from a mobile device of the user (e.g., transmitted from thetransmitter 1605). In at least one embodiment, the system 100 of FIG. 1and/or the system 1200 of FIG. 12 integrate with the system 1600 on amobile device.

The transmitter 1605 and receiver 1610 can each function as a client, aserver, and others. The transmitter 1605 and receiver 1610 can eachinclude the non-transitory computer-readable medium 1220 of FIG. 12 usedin operation. The non-transitory computer-readable medium 1220 of FIG.12 may include instructions that are executed by the transmitter 1605 orreceiver 1610 to cause the transmitter 1605 or receiver 1610 to performa method (e.g., a method disclosed herein). The transmitter 1605 andreceiver 1610 can engage in a communication with one another. Thiscommunication can be over a communication medium. Example communicationmediums include an intranet, an extranet, the Internet, a securedcommunication channel, an unsecure communication channel, radioairwaves, a hardwired channel, a wireless channel, and others. Exampletransmitters 1605 include a base station, a personal computer, acellular telephone, a personal digital assistant, and others. Examplereceivers 1610 include a base station, a cellular telephone, personalcomputer, personal digital assistant, and others. The example system1600 may function along a Local Access Network (LAN), Wide Area Network(WAN), and others. The aspects described are merely an example ofnetwork structures and intended to generally describe, rather thanlimit, network and/or remote applications of features described herein.

FIG. 17 illustrates at least one embodiment of a system 1700, upon whichat least one aspect disclosed herein can be practiced. In at least oneembodiment, the system 1700 can be considered a computer system that canfunction in a stand-alone manner as well as communicate with otherdevices (e.g., a central server, communicate with devices through datanetwork (e.g., Internet) communication, etc). Information (e.g., theinstruction 160 of FIG. 1) can be displayed through use of a monitor1705 and a user can provide information (e.g., goal input, locationinformation for the standard action for the user 150 of FIG. 1, etcetera) through an input device 1710 (e.g., keyboard, mouse, touchscreen, et cetera). A connective port 1715 can be used to engage thesystem 1700 with other entities, such as a universal bus port, telephoneline, attachment for external hard drive, and the like. Additionally, awireless communicator 1720 can be employed (e.g., that uses an antenna)to wirelessly engage the system 1700 with another device (e.g., in asecure manner with encryption, over open airwaves, and others). Amicroprocessor 1725 (e.g., that functions as the processor 1210 of FIG.12) can be used to execute applications and instructions that relate tothe system 1700. In one example, the microprocessor 1725 executes atleast one instruction associated with at least one of the differencecomponent 110 of FIG. 1, the instruction component 120 of FIG. 1, or theoutput component 130 of FIG. 1. Storage can be used by the system 1700,such as the microprocessor 1725 executing instructions retained by thestorage. The storage can be an example of the non-transitorycomputer-readable medium 1220 of FIG. 12. Example storage includesrandom access memory 1730, read only memory 1735, or nonvolatile harddrive 1740. In at least one embodiment, a memory (e.g., at least one ofthe random access memory 1730, read only memory 1735, and/or thenonvolatile hard drive 1740) retains instructions that cause a methoddisclosed herein to operate. In at least one embodiment, the memoryretains a database in accordance with at least one aspect disclosedherein.

The system 1700 may run program modules. Program modules can includeroutines, programs, components, data structures, logic, et cetera, thatperform particular tasks or implement particular abstract data types.The system 1700 can function as a single-processor or multiprocessorcomputer system, minicomputer, mainframe computer, laptop computer,desktop computer, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like.

It is to be appreciated that aspects disclosed herein can be practicedthrough use of artificial intelligence techniques. In one example, adetermination or inference described herein can, in at least oneembodiment, be made through use of a Bayesian model, Markov model,statistical projection, neural networks, classifiers (e.g., linear,non-linear, et cetera), using provers to analyze logical relationships,rule-based systems, deep intelligence, or other technique.

While example systems, methods, and so on have been illustrated bydescribing examples, and while the examples have been described inconsiderable detail, it is not the intention of the applicants torestrict or in any way limit the scope of the appended claims to suchdetail. It is, of course, not possible to describe every conceivablecombination of components or methodologies for purposes of describingthe systems, methods, and so on described herein. Therefore, innovativeaspects are not limited to the specific details, the representativeapparatus, and illustrative examples shown and described. Thus, thisapplication is intended to embrace alterations, modifications, andvariations that fall within the scope of the appended claims.

Functionality described as being performed by one entity (e.g.,component, hardware item, and others) may be performed by otherentities, and individual aspects can be performed by a plurality ofentities simultaneously or otherwise. For example, functionality may bedescribed as being performed by a processor. One skilled in the art willappreciate that this functionality can be performed by differentprocessor types (e.g., a single-core processor, quad-core processor, etcetera), different processor quantities (e.g., one processor, twoprocessors, et cetera), a processor with other entities (e.g., aprocessor and storage), a non-processor entity (e.g., mechanicaldevice), and others.

In addition, unless otherwise stated, functionality described as asystem may function as part of a method, an apparatus, a method executedby a computer-readable medium, and other embodiments may be implemented.In one example, functionality included in a system may also be part of amethod, apparatus, and others.

Where possible, example items may be combined in at least someembodiments. In one example, example items include A, B, C, and others.Thus, possible combinations include A, AB, AC, ABC, AAACCCC, AB, ABCD,and others. Other combinations and permutations are considered in thisway, to include a potentially endless number of items or duplicatesthereof.

1. A system, comprising: a score component configured to assign a scoreto at least one aspect of an actual action of the user; a selectioncomponent configured to select a training plan for the user from atraining plan set comprising a first training plan and a second trainingplan, where the selection is based, at least in part, on the score; andan output component configured to cause the selected training plan to bepresented to the user, by way of a display as a recommended trainingplan; a reception component configured to receive an indication, by wayof the display, that the user desires for the selected training plan tobe a current training plan for the user; and an appointment componentconfigured to appoint the selected training plan as the current trainingplan for the user in response to the indication that the user desiresfor the selected training plan to be the current training plan.
 2. Thesystem of claim 1, the component set comprising: a reception componentconfigured to receive a training plan change request to replace thecurrent training plan with a user chosen training plan; and an updatecomponent configured to replace the current plan with the user chosentraining plan, where the user chosen training plan is different from theselected training plan.
 3. The system of claim 1, where the at least oneaspect comprises a hand speed during at least part of the actual actionof the user, where the at least one aspect comprises a physical range ofmotion during at least part of the actual action of the user, and wherethe score accounts for the hand speed and the physical range.
 4. Thesystem of claim 1, where the sensor comprises a camera that captures theactual action of the user through video monitoring.
 5. The system ofclaim 1, where the training plan comprises an option for the user toview a video of a professional athlete.
 6. The system of claim 1, wherethe actual action of the user comprises a first sporting swing with apiece of sports equipment and a second sporting swing with the piece ofsports equipment.
 7. The system of claim 1, where the sensor capturesthe actual action of a user as a three dimensional action.
 8. The systemof claim 1, comprising: a disclosure component configured to causedisclosure of the score to the user.
 9. The system of claim 1, where thereception component is configured to receive an indication that the userdoes not desire for the selected training plan to be the currenttraining plan for the user and where the appointment component isconfigured to not appoint the selected training plan as the currenttraining plan in response to the indication that the user does notdesire for the selected training plan to be the current training plan.10. The system of claim 1, where the selected training plan comprises afirst instruction set directed at a first skill and a second instructionset directed at a second skill, where the output component is configuredto cause the first instruction set to be presented on the display, andwhere the output component is configured to cause the second instructionset to be presented on the display.
 11. The system of claim 10, anidentification component configured to identify when the user finisheswith the first instruction set, where the output component is configuredto cause the second instruction set to be presented on the display afteridentification that the user is finished with the first instruction set.12. A system comprising hardware communicatively coupled to a sensorthat captures an actual action of a user and performing a method, themethod comprising: evaluating the actual action of the user to producean evaluation result; making a recommendation of a first training planfor the user, from a training plan set comprising the first trainingplan and a second training plan, that is based, at least in part, on theevaluation result; receiving a rejection for the recommendation of thefirst training plan; and appointing the second training plan as thecurrent training plan of the user after the recommended training plan isrejected.
 13. The system of claim 12, the method comprising: disclosingthe second training plan to the user after the receiving the rejectionfor the recommendation; where the user provides the rejection for therecommendation of the first training plan and where the user designatesthe second training plan for appointment after the second training planis disclosed.
 14. The system of claim 12, the method comprising:prompting the user to submit feedback on the system; where the system isembodied upon a personal electronic device and where the feedback iscommunicated to a location remote to the personal electronic device. 15.The system of claim 14, where the feedback is based, at least in part,on the user's experience with the appointed training plan.
 16. Thesystem of claim 12, the method comprising: where making therecommendation of the first training plan comprises presenting the firsttraining plan to the user without presenting the second training planconcurrently to the user and where after receiving the rejection thesecond plan is presented to the user.
 17. A system, comprising: areception component configured to receive a motion sequence of an actualaction of the user swinging a piece of sports equipment; a choicecomponent configured to identify a motion sequence of a professionalathlete of the professional athlete swinging the piece of sportsequipment; and an output component configured to cause the motionsequence of the actual action of the user and the motion sequence of theprofessional athlete to be rendered concurrently on a display.
 18. Thesystem of claim 17, comprising an assessment component configured toassign a score to the actual action of the user, where the outputcomponent is configured to cause the score to be displayed concurrentlywith the motion sequence of the actual action of the user and the motionsequence of the professional athlete.
 19. The system of claim 18, aninstruction component configured to present a professional athlete setcomprising at least one professional athlete; and a selection componentconfigured to receive a user selection for the professional athlete fromthe professional athlete set, where the output component cause of themotion sequence of the professional athlete to be rendered concurrentlyon the display occurs in response to reception of the user selection forthe professional athlete.
 20. The system of claim 18, comprising: aninstruction component configured to recommend a focus for userimprovement with the piece of sports equipment, where the outputcomponent is configured to cause the recommended focus to be presentedon the display.